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Abstract Microscopic scrutiny aids in alkali-activated materials’ (AAM) application in construction industry. This study delves into the pore structure and properties of one-part alkali-activated slag (AAS) mortar modified by carbon black (CB) and recycled carbon fiber (rCF). The additives enhanced flexural strength by 51.82% (12.16 MPa) with lower water absorption (10.24%). Refinement of pore size and reduction of connectivity are key factors in improving properties. The densification effect of CB and the strong interface between rCF and gel were observed. Furthermore, AAS mortars exhibited multifractal characteristics within the range of micropores and capillary pores. Despite altering fractal regions, the additives did not affect its size dependence. The backbone fractal dimension increases with the addition of CB and rCF, exhibiting strong correlations with various macro properties, thus serving as a comprehensive parameter to characterize pore shape and distribution. This study deepens understanding of AAM composites, facilitating their adoption of low-carbon building materials.

Abstract Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state‐of‐the‐art models in terms of accuracy and computational cost.

This study presents designing and evaluating a reliable wireless power transfer (WPT) mechanism to charge electric vehicle (EV) batteries using resonant coupling. The EV wireless system was created applying the principles of mutual inductance whereby the receiving and transmitting coils were interlinked and connected to their circuits. Significant mathematical work was also done to obtain the equations for the impedance, the power input, and output power. Proteus software was employed to draw a Printed Circuit Board (PCB) for the reception of the receiver and the transmitter circuits. The simulations were conducted using ANSYS Maxwell and MATLAB softwares, utilizing the key parameters of the transmitter and the receiver coils. The experimental setup included an EV installed with transmitter and receiver circuits, copper coils, a compensation network of capacitors, and a 12 V battery voltage monitor. The study showed that the efficiency of wireless power transfer was 78% with the enhanced power density for the gap ranging between 6 and 20 cm. The innovations presented in this paper are a new lower operating frequency of 70 kHz, coil optimization, and a dynamic study on coil misalignment all of which enhance the efficiency and reduce cost of the proposed system.

Methylammonium Silicon Iodide (MASiI3), a novel perovskite material, has recently garnered significant attention for its potential to enhance the stability of solar cells. However, MASiI3-based perovskite solar cells (PSCs) have yet to achieve high performance. In this study, we utilized SCAPS-1D device modeling to investigate and optimize the performance of MASiI3-based PSCs, focusing on several key parameters. Our optimization efforts included the electron and hole transport layers (ETL and HTL), their respective thicknesses, doping densities (ND and NA), the TiO2/MASiI3 and MASiI3/Cu2O interface layers, series and shunt resistances, the metal back contact, and the influence of temperature. Through comparative analysis of various prospective materials, we identified TiO2 as the optimal ETL and Cu2O as the optimal HTL due to their superior band alignment. Our findings also highlighted the critical role of ambient temperature in maximizing power conversion efficiency (PCE). The proposed ETL and HTL materials demonstrated effective charge transport properties, contribute to enhanced device performance. Our optimized device achieved a Voc of 0.896 V, a Jsc of 42.47 mA/cm2, an FF of 76.94 %, and a remarkable PCE of 29.29 %. These results underscore the potential of MASiI3-based PSCs when utilizing TiO2 and Cu2O as charge-carrying materials and pave the way for further advancements in perovskite solar cell technology.

Background: Households are considered ideal settings for studying the transmission dynamics of an infectious disease. Methods: A prospective study was conducted, based on the World Health Organization FFX protocol from October 2020 to January,2021. Household contacts of laboratory-confirmed index cases were followed up for their symptomatic history, nasal swabs for RT-PCR,and blood samples for anti-SARS CoV-2 antibodies were collected at enrollment and days 7, 14 and 28. We estimated secondary attack rate (SAR), effective household case cluster size and determinants of secondary infection among susceptible household contacts using multivariable logistic regression. Results: We enrolled 77 index cases and their 543 contacts. Out of these, 252 contacts were susceptible at the time of enrollment. There were 77 household clusters, out of which, transmission took place in 20 (25.9%) giving rise to 34 cases. The acquired secondary attack rate (SAR) was 14.0% (95% CI 9.0–18.0). The effective household case cluster size was 0.46 (95%CI 0.33,0.56). Reported symptoms of nausea and vomiting (aOR, 7.9; 95% CI, 1.4–45.5) and fatigue (aOR, 9.3; 95% CI, 3.8–22.7) were associated with SARS-CoV-2 transmission. Conclusions: We observed a low SARS-CoV-2 secondary attack rate in the backdrop of high seroprevalence and asymptomatic transmission among households in Karachi, Pakistan.

This article methodically develops an improved self-regulating fuzzy-adaptive Sliding Mode Controller (SMC) that strengthens the disturbance compensation capacity of the nonlinear rotary pendulum systems while effectively attenuating the chattering content and curbing the control energy consumption. The article contributes to augmenting the SMC with online adaptation tools to achieve the said objectives. It employs the conventional Gao’s power-rate reaching law as the baseline. The scaling gain and the power rate of the said reaching law are adaptively modulated via a pre-calibrated two-input state-error-driven fuzzy nonlinear function. Additionally, the sign function in the law is also replaced with an odd-symmetric nonlinear fuzzy function to address the hard limits imposed by the former. Finally, the membership functions of the fuzzy function are self-regulated using the Extended-Kalman-Filter to improve the compensator’s adaptability in handling the system’s rapidly changing control requirements under exogenous disturbances. The aforementioned propositions are verified by performing customized and reliable hardware-in-loop experiments on the Quanser single-link rotary pendulum platform. As compared to baseline SMC law, the proposed control procedure contributes a ∼45.2%, ∼48.5%, and ∼34.8% reduction in position-regulation errors, control energy consumption, and peak overshoots, respectively. The experimental assessment validates the proposed control system’s enhanced robustness and chattering-suppression capability.

Abstract Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

Solar photovoltaic (PV) panels suffer from a reduction in performance due to dirt and environmental pollutants accumulating on their surface. As the number of solar panels installed grows, boosting their efficiency becomes more crucial to optimizing electricity output while decreasing the need for additional panels. Cleaning the PV panels can increase their efficiency, and an automated cleaning system with cutting-edge technologies can improve cleaning effectiveness. This article proposes a system that utilizes the Messaging Queuing Telemetry Transport protocol to enhance the efficiency of solar panels. Also, the system harnesses the power of IoT technology to automate the cleaning process. The system incorporates IoT-enabled sensors, actuators, and communication modules, all controlled by ESP-32. We programed the microcontroller using the software Arduino and Visual Studio Code. The Adafruit IO controls and monitors the system. Users can switch between fully automatic and manual modes using the Adafruit dashboard. The system utilizes DC motors, nylon brushes, a metal frame, a relay module, a buck converter, and a boost converter. A 10W solar panel powers the system with the help of a 20 A charge controller and a 12 V/2.5 Ah battery. The Adafruit dashboard fully controls and monitors the developed system. By automating the cleaning process and leveraging real-time data, the system maximizes solar power generation efficiency, minimizing downtime. The system increases the efficiency of tested 30 W solar panels by 30%.

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors’ operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy.

In organizations, the success of innovation efforts in improving performance relies heavily on entrepreneurial passion and the perceived importance of innovation. Understanding the link between deliberate practice and innovation broadens our current knowledge, emphasizing entrepreneurial passion as a driving force and the perceived importance of innovation as a crucial amplifying factor for performance. This study posits that deliberate innovation practice enhances innovation performance by fostering entrepreneurial passion, which is further influenced by the perceived importance of innovation. Grounded in self-determination theory and social cognition theory, this research collected data through a multisource, three-wave approach from software company employees. Utilizing SmartPLS (v 4.0) for measurement and structural model evaluation, the findings reveal that deliberate innovation practice positively impacts innovation performance. Entrepreneurial passion serves as a mediator between deliberate innovation practice and innovation performance, with perceived innovation importance strengthening this effect. This study demonstrates that the impact of deliberate innovation practice on performance is more pronounced when entrepreneurial passion mediates, especially at higher levels of perceived innovation importance. The findings offer essential theoretical insights and practical implications.

A tandem structure efficiently boosts solar cell efficiency because it uses a larger spectrum of solar radiation and minimizes photon energy thermalization. High-performance tandem solar cells have historically been difficult to produce due to a shortage of high-performance, low-bandgap cells. The proposed design is based on the CH3NH3GeI3 (1.9 eV) as a wide band gap absorber layer and FAMASnGeI3 (1.4 eV) as a narrow band gap perovskite layer with CZTSe as HTL. This research investigates the effect of different ETLs, perovskite layer thicknesses and their doping concentration on the device's photovoltaic parameters. Furthermore, the effect of tunnel junction thickness and front contact work function on the solar cell performance has also been simulated. It has been noted that the PCBM presents a better efficiency device than the other ETLs. The Voc of different ETLs has been related to Vbi. Moreover, the optimized thicknesses of both perovskite absorber layers are 450 nm and 400 nm for MAGeI3 and FAMASnGeI3, respectively. The optimal doping density is found to be E14 cm−3. It has been observed that the tunnel junction's thickness and the front contact's work function also play an important role in optimized device efficiency. The tunnel junction thickness is 4 nm for lead-free mixed cation perovskite/CZTSe tandem solar cells. However, the work function of the front electrode of 4.4 eV is optimum for this structure. The proposed lead-free mixed cation perovskite/CZTSe tandem device shows significant performance parameters, i.e., Voc= 0.970 V, Jsc = 28.46 mAcm−2, FF = 84.43 % and PCE= 23.32 %

Recent analyses by leading national wildfire and emergency monitoring agencies have highlighted an alarming trend: the impact of wildfire devastation has escalated to nearly three times that of a decade ago. To address this challenge, we propose FireDetXplainer (FDX), a robust deep-learning model that enhances the interpretability often lacking in current solutions. FDX employs an innovative approach, combining transfer learning and fine-tuning methodologies with the Learning without Forgetting (LwF) framework. A key aspect of our methodology is the utilization of the pre-trained MobileNetV3 model, renowned for its efficiency in image classification tasks. Through strategic adaptation and augmentation, we have achieved an exceptional classification accuracy of 99.91%. The model is further refined with convolutional blocks and advanced image pre-processing techniques, contributing to this high level of precision. Leveraging diverse datasets from Kaggle and Mendeley, FireDetXplainer incorporates Explainable AI (XAI) tools such as Gradient Weighted Class Activation Map (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) for comprehensive result interpretation. Our extensive experimental results demonstrate that FireDetXplainer not only outperforms existing state-of-the-art models but does so with remarkable accuracy, making it a highly effective solution for interpretable image classification in wildfire management.

Spontaneous subarachnoid hemorrhage (SAH) is the most severe form of hemorrhagic stroke and accounts for 5–7% of all strokes. Several chemical enzymes and cytokines are thought to cause reactions that may affect the mortality and morbidity of SAH patients. This study aimed to examine the possible relationships between these parameters and the occurrence of SAH and the clinical–radiological parameters in patients with acute SAH.

Gulcin Turkmen Sariyildiz,1,2 Canan Cicek Demir,3 Mehmet Emin Demir,4,5 Aykut &Idot;lker Arslan,6 Oktay Banli,7 Ali Do&gbreve;an Dursun8 1Operating Room Services, Vocational School of Health Services, Atilim University, Ankara, Turkey; 2Department of General Surgery, Medicana International Ankara Hospital, Ankara, Turkey; 3Department of Endocrinology, Internal Medicine, Faculty of Medicine, Atilim University, Ankara, Turkey; 4Department of Nephrology, Internal Medicine, Faculty of Medicine, Atilim University, Ankara, Turkey; 5Department of Nephrology, Medicana International Ankara Hospital, Ankara, Turkey; 6Department of Medical Microbiology, Faculty of Medicine, Atilim University, Ankara, Turkey; 7Department of General Surgery, Ankara Obesity Surgery Clinic, Ankara, Turkey; 8Department of Physiology, Faculty of Medicine, Atilim University, Ankara, TurkeyCorrespondence: Ali Do&gbreve;an Dursun, Atilim Universitesi Ahlatlibel Kampusu, Saglik Hizmetleri Meslek Yuksek Okulu, 1835. Caddesi No. 5, Cankaya, Ankara, 06805, Turkey, Tel +90 312 586 6902 ; +90 533 211 4821, Email ali.dursun@atilim.edu.tr; alidogandursun@gmail.comBackground: The excessive accumulation of fat tissue in obesity is the source of chronic low-level inflammation and causes future dysmetabolic and cardiovascular disorders. Removal of this excessive fat tissue with the aid of bariatric surgery (BS) techniques, such as sleeve gastrectomy, may reverse adverse inflammatory outcomes. The aim of this study is to investigate the impact of sleeve gastrectomy on inflammatory markers, specifically endocan, IL-6, and CRP, in individuals with obesity.Methods: Thirty-two patients with class 3 obesity and class 2 obesity + comorbidities were enrolled in the study. Clinical characteristics including age, comorbidity, body mass index (BMI), waist, and hip circumferences of the participants were noted before and 3 months after sleeve gastrectomy. Blood samples were collected during those periods to assess biochemical features such as serum endocan, interleukin-6 (IL-6), C-reactive peptide, fasting insulin, glycosylated hemoglobin A1c levels, and lipid panel. A statistical package program was used for the analysis of those parameters, and p< 0.05 was accepted as significant at a 95.0% confidence interval.Results: BMI reduced from 43.55± 6.78 to 36.16± 6.14 kg/m2 within 3 months following BS (p< 0.001). Preoperative serum endocan, IL-6, and CRP levels were correlated with BMI, and in line with BMI reduction, their serum levels decreased after BS (p< 0.05). HOMA-IR also reduced after BS, and both in the pre and post-BS periods correlated with BMI, IL-6, endocan, and CRP levels (p< 0.05). The mean total body weight loss was 20.4% within 3 months post-BS.Conclusion: BS techniques are effective in weight loss and reversing the inflammatory processes caused by obesity. Serum endocan, IL-6, and CRP levels are promising markers for describing obesity-related inflammation and objectively checking the alleviation of inflammation following BS.Keywords: bariatric surgery, endocan, interleukin-6, sleeve gastrectomy, weight loss

Hypoxia Inducible Factor-1 alpha and Hypoxia Inducible Factor-2 alpha (HIF-1α and HIF-2α) are crucial in Renal Cell Carcinoma (RCC) formation. Hypoxia Associated Factor (HAF) prompts HIF-1α degradation regardless of oxygen levels, but no such link exists for HIF-2α. This study encompassed 239 cases, where tissue microarray (TMA) sections were exposed to HIF-1α, HIF-2α, and HAF antibodies. Staining intensity and tumor cell percentage determined scores for HIF-1α, HIF-2α, and HAF, with median histoscore establishing "high" or "low" cutoffs. Among the cases, 64.9% (155 cases) were negative for HIF-1α, 17.6% (42 cases) displayed low, and another 17.6% (42 cases) showed strong HIF-1α expression. HIF-1α expression correlated significantly with histological type and World Health Organization/International Society of Urological Pathology (WHO/ISUP) nuclear grade. Regarding HIF-2α, 15.5% (37 cases) were negative, 23.4% (56 cases) exhibited low, and 61.1% (146 cases) displayed high expression, with larger tumor size in the high HIF-2α group. Among 239 cases, 41.4% (99 cases) were negative for HAF, 36.8% (88 cases) showed low, and 21.8% (52 cases) displayed high HAF scores. HAF expression correlated with WHO/ISUP nuclear grade and metastasis presence. Notably, HIF-2α staining intensity directly correlated with increased HAF intensity (rho=0.146; p=0.024), while HIF-1α intensity decrease corresponded with heightened HAF intensity, showing a statistically significant negative correlation (rho=-0.180; p=0.005). In this rare examination of tumor tissue, a reverse connection between HIF-1α and HAF expression was uncovered, while a linear link emerged between HIF-2α and HAF expression. Overall, this study established that HIF-1α, HIF-2α, and HAF expression are associated with an unfavorable prognosis. [Med-Science 2023; 12(3.000): 915-21]

Nano-biochar is a source of blackish carbonaceous material, a prerequisite for sustainable crop productivity. By using a variety of feedstock materials, nanobiochar synthesis can be employed via pyrolysis. Therefore, a project was initiated to explore the morpho-physio-biochemical alteration at the vegetative stage of wheat crops after the foliar application of nanobiochar suspension (NBS). This investigation was conducted at the Botanical Research Area of the University of Lahore in a randomized complete block design (RCBD) arrangement, with four treatments (0, 1, 3, and 5% NBS) by maintaining three replications for each treatment using the wheat variety “Zincol”. Nano biochar suspension in above mentioned concentrations were foliarly applied at the end of tillering/beginning of leaf sheath elongation of wheat seedlings to assess the morphological changes (root length, shoot length, number of leaves, fresh biomass/plant, dry biomass/plant), physio-biochemical alterations (total free amino acids, total sugars, chlorophyll content, protein, phenols, flavonoids), and nutrient uptake (Na, K, Ca, Mg, N, P contents. Our findings indicate that the foliar application of 3% NBS yielded the most favorable results across all measured attributes. Furthermore, Treatment-4 (5% NBS) specifically improved certain traits, including leaf area, total soluble proteins, and leaf calcium content. Finally, all NBS resulted in a decrease in carotenoid and sodium content in wheat seedlings.

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation.

Modern sheep breeding should be based on the production of high quality sheep products. Knowing the limits of animal productivity potential allows to manage the breeding process in the most effective way. Applying PCR-RFLP method makes it possible to identify allelic variants of genes associated with quantitative and qualitative characteristics of animals. By identifying different polymorphic variations and their phenotypic manifestations, one can create and replenish a database for managing the genetic progression of breeding-significant traits. The paper determines the gene polymorphism of growth hormone (GH), calpastatin (CAST) and growth differentiation factor (GDF9) of experimental animals and reflects an associative connection between the dynamics of growth and that of development of crossbred one-shear sheep of different origin (Russian meat merino ewes and imported producers). The article concludes that in order to increase sheep meat productivity, farms should give preference to animals carrying genes with the identified desired genotype on the studied polymorphisms and take into account their presence in the breeding process.

Multi-microgrids offer various benefits including the decreased overloading of a single microgrid, more options during faulty conditions, and more utilization of renewable energy resources. However, the implementation of a multi-microgrid brings the challenges such as power system complexity, interconnection issues, bidirectional power flow management, and power flow balancing. In the presence of these challenges, the power flow stability of the multi-microgrids is a challenging problem. In this context, this study evaluates a transient stability analysis model in multi-microgrids using solar photovoltaics, wind power, and a unified power flow controller (UPFC). UPFC offers a more robust power flow control strategy compared with other flexible alternating current transmission systems (FACTS) devices. First, a multi-microgrid structure consisting of the two microgrids was designed in DIgSILENT PowerFactory software. Second, the load flow calculation was performed in the absence and presence of UPFC, short circuit fault, and grid connection. Third, the electromagnetic transients (EMT) simulation was performed for all these situations. The results exhibited that the UPFC would offer significant power flow stability in the multi-microgrids. It was observed that the UPFC resulted in more transient stability in the microgrid where it was located. However, it improved the power flow quality at all the locations in the multi-microgrids. In addition, UPFC offered significant transient stability during the fault occurrence. The results offer various insights into power flow management in multi-microgrids.

Conclusion: The sheathless TR approach showed lower rates of RAS and femoral conversion. Sheathless has a higher success rate; however, it also increased RAO. Thus, more trials are needed to confirm this technique’s long-term outcomes and complications.

Forecasting the flow of rivers is an essential task in water management resources. However, the flow changes significantly at the seasonal variation of the year, so it is difficult to predict the flow because of its complex nature. The study aims to find a predictive model for forecasting the inflow of the Indus River. Two multivariate models, the autoregressive distributed lag (ARDL) model and vector error correction model (VECM), are compared to examine whether there is any long- or short-run association between variables. It is observed that the ARDL model shows long- and short-run associations between inflow, outflow, and levels. In contrast, VECM shows only a long-run association between the variables. It is concluded that the ARDL model performs better in predicting the future inflow of the Indus River than VECM based on the stability test or accuracy of the models.

Drawing on the associative network memory theory (ANMT), this study aims to investigate the impact of social media marketing activities (SMMAs) on brand equity (BE) of luxury brands through the mediating role of brand image (BI) and the moderating role of word of mouth (WOM). This study employs a cross-sectional design to collect data from online adult users of Weibo in China. Data are analyzed in SmartPLS (v. 4.0.8.4) to evaluate the measurement model and the structural model. Findings of this study reveal that SMMAs have a significant positive impact on BI and BE. Besides, BI partially mediates the relationship between SMMAs and BE. Moreover, WOM moderates the association between SMMAs and BE such that the association is more potent at high levels of WOM and vice versa. This is one of the few studies that investigate the role of SMMAs on BE in the context of luxury brands, mediated by BI. Also, this is the first study that explores the moderating role of WOM in the relationship between SMMAs and BE through the mediating role of BI.

Purpose: Even though there is an immense level of research related to the impact of product quality and service quality on consumers’ purchase intentions the role of satisfaction as the mediator between perceived product qualities, affective commitment, and repurchase intentions has not been cross-examined in previous literature. To understand how this mediating mechanism affects repurchase intentions, we advanced a conceptual model. Design/Methodology/Approach: This study employed a quantitative research approach. Data were obtained using the convenience sampling method from a sample of 400 respondents. Statistical Package for the Social Sciences (SPSS) version 22.0 and Analysis of Moment Structure (AMOS) version 22.0 were used for the data analysis. Findings: Results show that the relationship between perceived product quality, affective commitment, and repurchase intentions is partially mediated by customers’ satisfaction. Implications/Originality/Value: understanding the underlying process of repurchase intentions of customers. The study is one of the exceptional efforts to provide evidence regarding the role of customer satisfaction as a mediating variable between independent and dependent variables.

Plastic waste is an immense challenge for waste management and sustainable development because it has been detrimental to aquatic and terrestrial life. The easiest and most common method to eliminate huge volumes of this waste is burning, which is harmful to environment. Plastic waste in bricks can reduce waste accumulation problems and partially replace natural resources (clay) with waste. This study mainly focused on utilizing waste plastic dust in fired clay bricks and investigating its effect on their physical and mechanical properties, such as compressive strength, water absorption, and density conforming to ASTMC67. The brick specimens were prepared for this purpose with varying percentages of plastic dust (0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%) by mass. By increasing the plastic dust percentage, the compressive strength was reduced, and water absorption increased. However, both remained within an acceptable range and passed the criteria of most building standards. The bricks produced were lightweight and exhibited considerably low shrinkage, leading to volume stability. The optimum percentage of plastic was found to be 7.5%, providing better results. It is corroborated that waste plastic dust can be utilized to manufacture relatively lightweight fired clay bricks with higher volume stability. This method can be adopted in areas where plastic is burnt in the open air to resolve plastic waste accumulation issues.

Abstract In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness.

In previous research simulating steelmaking ladles using cold water models, the dosage/volume of the salt tracer solution is one of the factors that has been overlooked by researchers to a certain extent. Previous studies have demonstrated that salt tracers may influence the flow and measured mixing time of fluids in water models. Based on a water model scaled down from an industrial 130-ton ladle by a ratio of 1:3, this study investigates the impact of salt tracer dosage on the transport and mixing of tracers in the water model of gas-stirred ladle with a moderate gas flow rate. A preliminary uncertainty analysis of the experimental mixing time is performed, and the standard deviations were found to be less than 15%. It was observed in the experiments that the transport paths of tracers in the ladle can be classified into two trends. A common trend is that the injected salt solution tracer is asymmetrically transported towards the left sidewall of the ladle by the main circulation. In another trend, the injected salt solution tracer is transported both by the main circulation to the left side wall and by downward flow towards the gas column. The downward flow may be accelerated and become a major flow pattern when the tracer volume increases. For the dimensionless concentration curve, the sinusoidal type, which represents a rapid mixing, is observed at the top surface monitoring points, while the parabolic type is observed at the bottom monitoring points. An exception is the monitoring point at the right-side bottom (close to the asymmetric gas nozzle area), where both sinusoidal-type and parabolic-type curves are observed. Regarding the effect of tracer volume on the curve and mixing time, the curves at the top surface monitoring points are less influenced but curves at the bottom monitoring points are noticeably influenced by the tracer volume. A trend of decreasing and then increasing as the tracer volume increases was found at the top surface monitoring points, while the mixing times at the bottom monitoring points decrease with the increase in the tracer volume.

Objective: To know the pattern of skin disorders amongst patients reporting to the Dermatology outpatient of a tertiary care hospital. Study Design: Cross-sectional study. Place and Duration of Study: Dermatology OPD Combined Military Hospital, Bahawalpur Pakistan from May to Oct 2022. Methodology: Eight thousand sixty-eight patients attending the outpatient department (OPD) for skin problems were included in the study. Consultants with Postgraduate qualifications in Dermatology diagnosed all cases. Diagnosis of skin problems were noted for each patient. Diseases were categorized into various groups. Results: The mean age of patients was 29.04±15.941 years. 54.35% of patients (n=4385) were in the 20-40 years age range. Infections were the most commonly seen disease group (n=2152,26.7%), followed by dermatitis and eczematous disorders (n=1784,22.1%), parasitic diseases (n=1341,16.6%) and acquired pigmentary disorders (n=579,7.2%) in descending order of frequency. Together, these four groups of diseases accounted for 72.6% of total patients. Infections and infestations accounted for 43.3% of patients reporting to our outpatients during the study period. Scabies was the most common disease in 15.6% (n=1262) of our patients. Scabies and dermatophyte infections were the two most commonly encountered diseases. These two diseases comprised 26.5% (n=2141) of the total cases. Conclusion: Infections and infestations accounted for 43.3% of patients reporting to our outpatients during the study period. Scabies and dermatophyte infections were the two most commonly encountered diseases. Public health education regarding personal hygiene and dermatological care availability can help lessen the burden of skin diseases.

Grapes (Vitis vinifera L.) are highly valuable crops enriched with minerals, vitamins, phenolics, and antioxidants required for daily human activities and to prevent cancer and cardiovascular diseases. The objective of this study was to elucidate the physicochemical and mineral evaluation of seven neglected local grape cultivars (‘Kishmish’, ‘Sra Kishmish’, ‘Askari’, ‘Sahibi’, ‘Haita’, ‘Sundar Khani’ and ‘Toran’) growing in Baluchistan, as it can help in future breeding, processing, and species conservation. Therefore, these cultivars were evaluated for different physical, biochemical, and availability of macro– and micro–mineral contents. In physical evaluation, the local cultivar ‘Haita’ showed maximum bunch length (19.72 cm) and bunch width (11.88 cm), while the highest bunch weight (527.33 g) was recorded in ‘Sahibi’. Similarly, significant differences were noted in biochemical traits, and maximum total soluble solids (24.76%) and titratable acidity (1.58%) were observed in ‘Sundar Khani’, while the highest vitamin C (26.17 mg 100 g–1), total sugars (30.26%) in Sundar Khani, and nonreducing sugars (8.79%) were recorded in ‘Haita’. The cultivars growing in Balochistan also showed variations in phenolics (113.79–346.50 mg GAE L–1) and antioxidants (85.77– 90.87%). Likewise, the concentrations of macro- and microelements were also highly variable in these cultivars. Overall, among these cultivars, ‘Toran’ performed better in the agroclimatic conditions of Balochistan, as it has high yield attributes such as berry length, width, and weight. Moreover, it was also enriched with total soluble solids, antioxidants, and calcium contents.

This article presents a techno-economic analysis of upgrading a Photovoltaic (PV) power system at large. A comparative study has been carried out considering various technical and economic factors like per unit cost, annual load, energy demand growth, and PV panel efficiency, annual performance degradation of system, cost of electricity, payback period, initial capital expenditure, and rate of return in the form of saving in energy bills. The PV system is proposed to be upgraded from 1 MW to 2 MW at the National University of Sciences & Technology (NUST), Islamabad. The PV system is subject to be upgraded due to the growth rate of annual load and amount saved by PV contribution, which has been forecasted using data trends of the above-mentioned technical and economic factors. While considering these factors, four economic models have been evaluated and compared, which include Self-Built via Contractor with self-financing, Self-Built with bank financing, Power purchase agreement (PPA), also known as rental agreement and Built, Own, Operate & Transfer (BOOT) model. Merits and demerits of these models have been discussed, and the most feasible model has been proposed based on the mentioned key factors and long-term sustainable development goals (SDG) for affordable clean energy to contribute to climate action, i.e., adaptation of renewable energy resources (RER) and reduction of greenhouse gases (GHG).

In this review paper, the anti-surge control (ASC) of the compressors and fault-tolerant control (FTC) systems are described from the perspective of integration for reliability enhancement against faults in the system components. It explains the phenomenon of a surge in the compressors, surge precursors, and the potential damage caused by this surge. The explanation of surge avoidance methods in compressors incorporated with modern surge control systems is described along with their applications. The sizing of the system components and valves, particularly methodologies for appropriately estimating acceptable upstream pipe sizes, are elaborated. The existing surge protection techniques for compressors are analyzed to highlight the advantages and disadvantages and from a future perspective, new approaches for detecting system changes and surges, are included. In the end, the concept of fault tolerance and its advanced applications concerning the anti-surge control for compressors are explained. This study contributes to the young researchers in the field of anti-surge control systems for compressors with the integration of fault tolerance to increase the reliability of the system.

The Matrix converters (MCs) are widely used in a large number of applications such as Aircraft, Submarines, and AC drives. In these applications, because of the many chances of occurrence of a critical fault that leads to a halt of the system, and due to the failure of power electric switches, the MC operations can be compromised and there is a fear of burning of the components caused by the whole system shutdown. Therefore, a Fault-Tolerant Control System (FTCS) is extremely necessary for the continued operations of the Three-Phase Matrix Converter (TPMC) to improve the reliability and productivity of the system under faulty conditions. In this paper, an advanced FTCS is proposed based on detection, and dual hardware redundancy for TPMC. Two types of faults are injected into the system to observe the performance of the proposed system: internal open circuit faults on switches and external short circuit faults at the load side. A Fault Detection and Isolation (FDI) unit is used to detect and isolate the faulty switches using Artificial Neural Networks (ANN), and dual hardware redundancy in the switches has been proposed for fault tolerance. In case of external fault, a load-side fault detector is implemented using ANN. The simulation results in MATLAB/Simulink environment show the accurate and stable working of TPMC under faulty conditions, and hardware-in-the-loop is implemented with STM32-Nucleo-F103RB board to verify open circuit fault results. The proposed dual redundant FTC with FDI unit offers an excellent solution for the continued performance of TPMC which ultimately enhances the reliability of the system.

Tick-borne maladies of canines are increasing in sub-tropical and tropical areas around the globe. Theileriosis is an emerging issue in canines, especially dogs but the data regarding the disease prevalence in Asian countries is scarce. Therefore, the current study was designed to check the molecular prevalence of Theileria spp. in dogs of Lahore, Pakistan. A total of 102 dog blood samples were initially screened by microscopy and later on by PCR. The results revealed that 10.78% (11/102) and 6.86% (7/102) of pet dogs were found positive by PCR and microscopy, respectively. The phylogenetic analysis showed the sequences resemblance up to 98-99% with cytb gene fragments of Theileria annulata isolates from Iran, India, Turkey, Sudan, China, and Tunisia. Assumed risk factors revealed a significant (P

Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on the Twitter dataset and the robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively.

Metabolomics has been used as a powerful tool for the analysis, and drug-lead identification in medicinal plants and herbal medicines. For the coverage of a broader range of plant-based metabolites using LC-MS, one of the important parameters is the selection of analysis mode and data processing for identification. This paper describes the utility of two distinctive acquisition modes in combination, a classic data-dependent acquisition (DDA) mode and a data-independent acquisition (DIA) mode for obtaining the mass spectrometry data of plant extracts using LC-ESI-QTOF/MS. Tribulus terrestris plant was used as a case study. We have applied three-step data analysis pipeline 1-annotation and putative identification of metabolites, 2-validation, and relative quantification, and 3-multivariate analysis using open-access MS-DIAL, Skyline, and Perseus software. A total of four samples of T. terrestris (aqueous extracts), two fruits, and two whole plant samples, from two different regions, were used. By combining data analysis results of plant fruit samples from two different regions, a total of 95 and 77 metabolites were identified in positive and negative ionization modes, respectively. Similarly, in the analysis of the whole plant from two different regions, 75 and 76 metabolites were identified in positive and negative ionization modes, respectively. We suggested the use of DDA mode for annotation, identification of metabolites, and generation of transition lists in MS-DIAL, furthermore, the use of DIA acquisition mode for enhancing metabolites sensitivity in complex samples, deconvolute MS1/MS2 spectra in Skyline for the quantitative performance and analytical reliability. The developed protocol can be used for the broader coverage of plant-based metabolites.

Summary: Mitochondrial DNA copy number (mtCN) is often treated as a proxy for mitochondrial (dys-) function and disease risk. Pathological changes in mtCN are common symptoms of rare mitochondrial disorders, but reported associations between mtCN and common diseases vary across studies. To understand the biology of mtCN, we carried out genome- and phenome-wide association studies of mtCN in 30,666 individuals from the Penn Medicine BioBank (PMBB)—a diverse cohort of largely African and European ancestry. We estimated mtCN in peripheral blood using exome sequence data, taking cell composition into account. We replicated known genetic associations of mtCN in the PMBB and found that their effects are highly correlated between individuals of European and African ancestry. However, the heritability of mtCN was much higher among individuals of largely African ancestry (h2=0.3) compared with European ancestry individuals(h2=0.1). Admixture mapping suggests that there are undiscovered variants underlying mtCN that are differentiated in frequency between individuals with African and European ancestry. We show that mtCN is associated with many health-related phenotypes. We discovered robust associations between mtDNA copy number and diseases of metabolically active tissues, such as cardiovascular disease and liver damage, that were consistent across African and European ancestry individuals. Other associations, such as epilepsy and prostate cancer, were only discovered in either individuals with European or African ancestry but not both. We show that mtCN-phenotype associations can be sensitive to blood cell composition and environmental modifiers, explaining why such associations are inconsistent across studies. Thus, mtCN-phenotype associations must be interpreted with care.

Gastrointestinal histoplasmosis remains an inconspicuous clinicopathologic entity. It is predominantly considered a protean manifestation of disseminated disease. We hereby delineate a unique case of biopsy-proven isolated colonic histoplasmosis in a patient undergoing methotrexate therapy. Furthermore, we present the first systematic review of the MEDLINE, Google Scholar, Embase, and Scopus databases regarding isolated colonic histoplasmosis in adult patients receiving immunomodulator therapy (IMT). A total of 13 case reports (level of clinical evidence: IV) were identified. The mean age was 55.6 ± 11.1 years, with 9 (69.2%) cases reported in women. Patients with subclinical disease (5, 38.5%) were often incidentally diagnosed by screening colonoscopy. Symptomatic individuals predominantly presented with diarrhea (4, 30.8%), weight loss (3, 23.1%), and/or abdominal pain (3, 23.1%). IMT was mainly administered for liver transplant (4, 30.8%), renal transplant (4, 30.8%), and ulcerative colitis (2, 15.4%). Common colonoscopy features included colonic ulcerations (7, 53.8%), polyps or pseudopolyps (3, 23.1%), and/or mass-like lesions (3, 23.1%). Diagnosis was made by histology of colonic biopsy in 11 (84.6%) and resected specimens in 2 (15.4%) patients. Treatment consisted of a combination of amphotericin B with oral itraconazole in 6 (46.2%), oral itraconazole alone in 5 (38.5%), and amphotericin B alone in 2 (15.4%) patients. Complete clinical recovery was achieved in all patients. This article illustrates that isolated colonic involvement can be the only clinical presentation of histoplasmosis. It may masquerade as other bowel disorders, presenting diagnostic and therapeutic conundrums. Gastroenterologists should rule out colonic histoplasmosis in IMT recipients who develop unexplained colitis symptoms.

Two-dimensional (2D) structures show atomic geometry of ultrahigh thickness and have the highest surface-to-volume ratio. A monolayer of 2D materials commonly comprises atomically thick covalent lattice bonds, which contain free nanosheets with dangling bonds that might exhibit odd electrical and optical properties. The gathering for 2D derivatives of vdW architectures may be viewed as being outside the bounds of lattice matching due to the lack of directly chemical bond proficiency. Heterostructures are frequently composed of multiple parts associated with significant interfaces and are extensively studied to prevent problems caused by hybrids with unique functions, such as tunneling and confinement effects. To optimize the adsorption/desorption energy for important reactions and to advance the kinetics of chemical reactions, heterostructures formation is a strong strategy to accelerate Hydrogen evolution reaction (HER) activity is proposed. This mini-review deals with various 2D material and their heterostructures, from catalysts design to enhanced photo and electrocatalysis for H2 evolution reactions. Various forms, including 2D/2D, 1D/2D, and 0D/2D of heterostructures, are explained in water splitting reaction. By thoroughly addressing the fundamental aspects, recent developments, and associated challenges ⁠— the author's recommendation in compliance with future contests and prospects will pave the way for readers.

This paper presents a home-built projection lithographer designed to transfer the image from a DLP (digital light processing) projector MEMS matrix onto the microscope objective’s field of view, where a photoresist-covered substrate is placed. The photoresist is exposed using blue light with a wavelength of 450 nm. To calibrate the device and adjust focal lengths, we utilize a red light that does not affect the photoresist. The substrate is located on a movable platform, allowing the exposure field to be shifted, enabling the exposure of designs with lateral sizes of 1 × 1 cm2 at a resolution of a few micrometers. Our setup showcases a 2 μm resolution for the single frame 200 × 100 μm2, and a 5 μm resolution for 1 × 1 cm2 with field stitching. The exposure speed, approximately 1 mm2/100 s, proves to be sufficient for a variety of laboratory prototyping needs. This system offers a significant advantage due to its utilization of easily accessible and budget-friendly components, thereby enhancing its accessibility for a broader user base. The exposure speed and resolution meet the requirements for laboratory prototyping in the fields of 2D materials, quantum optics, superconducting microelectronics, microfluidics, and biology.

Usually, polyphenols help address numerous health issues caused by oxidative stress. Tea is a popular beverage (rich in polyphenols) with abundant health promoting and disease prevention with great health-promoting and disease-prevention attributes, originating from the delicate, dried leaves of the Camellia sinensis plant. Tea has been proven to have health-boosting impacts like anti-inflammatory, anti-cancerous, anti-diabetic, and aids in weight loss. Cognitive impairment, also known as cognitive decline caused by aging or other neurological disorders, has become an emerging health concern. Tea polyphenols, especially phenolic acids, havegained enormous attention due to their link to improved cognitive function by preventing cognitive decline. This review summarizes recent studies on the health benefits of polyphenols in tea. Additionally, effective traditional and modern techniques to extract polyphenols and their effects on various diseases have been described.

The unforgettable COVID-19 shock is most likely to be reversed by a viable vaccination strategy. In this paper, we investigate willingness to be vaccinated (WTV) against the COVID-19. Current trends suggest that only around 73% of EU inhabitants (15 and +) were immunized, with more than 104 million people still warranted to be immunized. Vaccine reluctance is a key impediment to conducting immunization programs in the setting of a pandemic. We provide first of its kind empirical evidence on the citizens (N = 11,932) of the EU-27 by employing the recent data from the European Commission. Based on the survey responses, controlling for the correlations in the error terms, we utilize a simulated multivariate probit regression model. Our results show that amongst all the statistically significant drivers of the WTV, the positive perception (vaccination works and has no side effects); R&D information (clarity on how vaccination is developed, tested, authorized) has the largest impact on the WTV. We find that the group of variables on social feedback (Positive perception; social adoption and pressure), and on trustworthy sources of information (R&D info; medical advice) are to be considered for WTV policy. The counteracting policy gaps that act against WTV include vaccination governance dissatisfaction, perception of long-term side effects, growing mistrust in information sources, uncertainty between safety and efficacy, education level, and risky age group. Strategies based on the outcomes of this study are needed to address public acceptance and willingness to vaccinate during a pandemic. This research is novel and offers authorities in-depth insights into the challenges and solutions regarding the COVID-19 pandemic and thus to its end via stimulation of the WTV.

Objective: The present study aimed to assess the prevalence of various types of foot ulcers among diabetic patients and their association with age and gender. Methods: This prospective cross-sectional study was conducted at the Medicine Department of Pakistan July to December 2019. A total of 133 diabetic patients presenting at the study site for foot ulcer treatment were recruited. The foot ulcers were classified as neuropathic, ischemic ulcer, or neuroischemic, and the severity/degree of the ulcer was determined using an internally developed ten points scoring system. The data were analyzed using SPSS version 22.0. Results: There was a high prevalence of neuropathic ulcers (69.2%) among the enrolled diabetic patients, followed by ischemic ulcer (33.1%) and neuroischemic (24.8%). The mean neuropathic scores were significantly high among males (4.84 ± 2.09) than females (4.02 ± 1.78) (p=0.02). It was also higher in those aged 50 years or greater (4.76 ± 1.91) as compared to those with less than 50 years of age (3.79 ± 2.24) (p=0.036). Conclusion: The most prevalent type of diabetic foot ulcer was the neuropathic type. Increased severity of these foot ulcers was observed with advancing age.

Sun drying in the open air is quite popular worldwide. However, the use of solar dryers to preserve various perishable agricultural products is a relatively new area of study, and the long-term effects of this method are not yet fully understood. The slow drying process in direct sunlight can contaminate the dried materials by soil and insects. To overcome these challenges, we devised a sun drying system that included a heating part, a drying area, a portable stand, fans, and a 50-W photovoltaic panel. An alternate energy source was used to power the drying process during cloudy days and at night. Fresh Freestone peach, Golden apple, and Anaheim chilies weighing 10 kg each with the initial moisture content of 89%, 87%, and 75% on a wet basis (w.b), respectively, were used in the experiments. The final moisture content of the samples was reduced by an average of 16%, 15%, and 11% for Freestone peaches, Golden apples, and Anaheim chilies, respectively. The quality analysis was carried out to determine sample composition, total bacteria, and color of dried products. The results indicated that the dried products met the recommended quality standards for food products in terms of composition, total bacteria, and color. This research supports the use of a hybrid mixed-mode solar dryer for drying a wide range of perishable agricultural products.

This paper proposes a novel hybrid fault-tolerant control system (HFTCS) with dedicated non-linear controllers: artificial neural network (ANN) and sliding mode control (SMC) for active and passive parts, respectively. The proposed system can provide both desirable properties of stability to unexpected fast disturbances and post-fault optimal performance. In the active fault tolerant control system (AFTCS) part, the fault detection and isolation (FDI) unit is designed through the use of ANN for the estimation of faulty sensor values in the observer model. In the passive fault-tolerant system (PFTCS) part, the air-fuel ratio (AFR) controller is designed using a robust SMC that allows systems to manage faults in predefined limits without estimation. In the proposed system, SMC will form the passive part to react instantly to faults while ANN will optimize post-fault performance with active compensation. Moreover, Lyapunov stability analysis was also performed to make sure that the system remains stable in both normal and faulty conditions. The simulation results in the Matlab/Simulink environment show that the designed controller is robust to faults in normal and noisy measurements of the sensors. A comparison with the existing works also demonstrates the superior performance of the proposed hybrid algorithm.

IntroductionThe current study anchors on the social identity theory (SIT) and social exchange theory (SET) to investigate the association between green talent management (GTM) and employee retention (ER), mediated by green organizational identity (GOI). Further, the study projects the moderator effect of green shared vision (GSV) in the direct association between GTM and GOI, and the indirect link between GTM and ER through the mediator effect of GOI.MethodsWe collected time-lagged (i.e., three-wave) data from 495 frontline managers in the tourism service firms in Pakistan. Data are analyzed using SmartPLS SEM (V 3.3) to evaluate the measurement and structural models.ResultsOur results support all the projected associations and confirm the direct relationships between GTM and ER (β = 0.480, CIs = 0.494, 0.578), GTM and GOI (β = 0.586, CIs = 0.517, 0.670), and GOI and ER (β = 0.492, CIs = 0.425, 0.566). The findings further reveal that GOI significantly mediates the relationship between GTM and ER (β = 0.257, CIs = 0.184, 0.312). In addition, the moderator effect of GSV significantly underpins the direct association between GTM and GOI (β = 0.512, CIs = 0.432, 0.587) and the indirect association between GTM and ER, mediated by GOI (β = 0.526, CIs = 0.441, 0.590).DiscussionThis is the first study that explores a moderated mediation model to explain when and how tourism service firms can promote ER through inculcating GTM strategies. The findings indicate that service firms in the tourism industry must develop and retain green talent to exploit pro-environmental strategies.

Asthma is a major non-communicable disease (NCD) affecting adults and children, among whom it is the most common chronic disease reported all around the globe1 Asthma has been a significant cause of hospitalizations and financial burdens for low and lower- middle -income countries like Pakistan. According to the latest WHO data published in 2020, asthma deaths in Pakistan reached a staggering 20,750 or 1.42% of total deaths.2 Many etiologies including social and environmental factors, genetic predispositions, and ambient air pollution, influence asthma morbidity and mortality. In particular, the worsening air quality index in the country needs to be a point of focus as a significant cause of asthma related morbidity. The rapid decline in air quality levels in Pakistan, particularly in major industrial cities such as Lahore and Karachi, is a cause of grave concern. For example, air quality in Lahore has declined over the last two decades: in 1998, its pollution concentration was 33 µg/m³. By 2016, it had doubled to 64 µg/m³—more than six times the WHO guideline.3 Similarly, Karachi, the largest city in Pakistan, has a PM2.5 concentrations, which is 3.4 times the WHO annual air quality guideline value, an alarming scenario for a city where environmental policies and infrastructure is not in place.4 A study in Lahore noted the role of ambient air pollution among various population groups of the city. The research showed a positive Pearson correlation between environmental air pollutants and the number of asthma patients visiting the OPD.5 However, a similar, comprehensive study has not been conducted in Karachi on the same level. As in most cases, the first step toward solving a problem is to recognize it. Hence there’s a need for more research to be conducted to determine the number of hospitalizations of asthmatic patients caused by low air quality as it will give us a better understanding of the true extent of the issue at hand and allow more appropriate prioritization, in the context of public health by the government. Once this is done, resources can be allocated more efficiently towards areas and populations suffering more acutely to relieve the suffering of the majority. In addition, educational campaigns can be designed considering the population’s educational demographics and general knowledge regarding the exact reasons for the worsening air quality in those areas. ---Continue

A lot of research is being carried out on the Direct Current (DC) servo motor systems due to their excessive applications in various industrial sectors owing to their superior control performance. Parameters of the DC servo motor systems to be used in the simulation software are usually unknown or change with time and have to be determined accurately for obtaining the precise simulation response. In this paper, three different estimation techniques for multi-domain DC servo motor model parameters are discussed namely the Compare Coefficient Method, MATLAB Parameter Estimation Toolbox, and System Identification Toolbox. The paper performs a comparison of these methods to identify the one that gives the most accurate results. Experimental data has been used for the comparison of the estimated response from the techniques. The results show that the parameters obtained from the parameter estimation method give the most accurate simulation results with the least error against the experimental results. The study is significant for guiding researchers to prefer this method for estimation purposes of DC servo motor simulation model parameters. The presented technique, i.e. parameter estimation technique, is relatively less complex and requires less computational cost as compared to other techniques found in the literature.

Background and objective: Breast cancer is the most common malignancy in females. Early diagnosis can help in reducing the morbidity and mortality associated with this disease. Sonoelastography has shown to be a valuable tool in early detection of breast cancer. The aim of the study was to evaluate the diagnostic accuracy of combining sonoelastography with mammography in diagnosing malignant solid breast lesions taking histopathology as a gold standard.Methods: A descriptive cross-sectional study was carried out at the Department of Radiology, Shaikh Zayed Medical Complex/Federal Post-Gradaute Medical Institute (FPGMI), Lahore, Pakistan during six months period from 1st September, 2018 to 31st March, 2019. Female patients > 35 years of age and presenting with suspected malignant breast masses were included. Mammography and sonoelastography of the lesions were done. Finally, the patients were referred for tru cut biopsy. Diagnostic accuracy was calculated for mammography, sonoelastography and their combination.Results: A total of 200 female patients were enrolled with a mean age of 50.8 (± 8.6) years. Mammographic findings suggestive of malignancy (BI-RADS category IV and V) were present in 96 (48%) cases. Elastography showed finding of malignancy (Score 4 and 5) in 96 (48%) cases. Combined sonoelastography and mammography diagnosed malignancy in 112 (56%) cases. Histopathologically confirmed malignancy was seen in 110 (55%) cases. The sensitivity and specificity of mammography (76.3 % and 77.7%) and sonoelastography (77.3% and 87.7%), were comparable. The combination of mammography and sonoelastography showed a higher sensitivity (90.9%) and specificity (86.6%). The combination showed a diagnostic accuracy of 89% followed by sonoelastography (82%) and mammography (77%) alone.Conclusion: Combination of sonoelastography and mammography can significantly enhance the diagnostic accuracy of breast carcinoma. Females may be offered mammography or ultrasonography in combination with sonoelastography for routine testing.